When capturing an image with a digital camera, the source of the illumination for the scene affects the colors captured with the camera. For indoor scenes the illumination source can vary widely and can include a tungsten bulb, halogen lamps, fluorescent lamps, sunlight coming in through a window, or even a xenon light. Each of these types of light sources has a different spectral energy distribution. The types of light sources that create light using a filament glowing at a high temperature (for example tungsten bulbs) are typically characterized by a color temperature defined as a Planckian radiator with a temperature of 50 degrees higher than the filament of the light. The sun can also be characterized as a Planckian radiator but the loss of some wavelengths through scattering and absorption in the atmosphere causes significant differences from the Planckian radiator at those wavelengths. Because of the variation in the spectral power distribution of the sun, standard spectral power distribution curves have been developed. One of the standard curves is called D65 corresponding to a color temperature of 6500K. Clouds in the sky can also affect the spectral distribution of energy reaching the scene from the sun. The time of day also affects the color temperature of the sun (noon vs. sunrise). The color temperature can be affected by whether the object is in direct sun light or in shadows.
The types of light sources that excite a phosphor layer that then fluoresces (for example fluorescent lamps and xenon lamps) tend to have spectral distributions that are unique to the phosphors in the lamp in combination with the mercury vapor spectrum.
Each of these light sources has a different spectral power distribution that affects the colors captured in a scene by a camera. For example when you have a white object illuminated by a tungsten bulb the white object will appear yellow in the scene captured by the camera. This is because the tungsten bulb does not produce much blue light. A white object is an object that reflects a similar amount of the red, green and blue light that hits the object. When a white object is illuminated by a tungsten bulb more red light is hitting the object than blue light and therefore more red light is reflected, causing the object to look yellow to the camera. The human eye adjusts to different Illuminant and compensates for the color shift but a camera records the actual light in the scene.
Fortunately these color shifts caused by the illumination source can be corrected. This correction is typically called white balancing. For proper white balancing the illuminant of the scene must be known. There are a number of methods currently used to try to determine the scene illuminant to be used in white balancing.
One method looks for the brightest point in a scene and assumes that it should be white. The brightest point is then adjusted until it is white and then this adjustment is used to balance the rest of the scene. This method operates on the assumption that the brightest point in a scene is from a white object or from a specular reflection. For example the specular reflection coming from a car windshield. Obviously not all scenes have the brightest point as a specular reflection or a white object. When this method is used on a scene with a non-white object that is the brightest point in the scene it can result in significant color mismatch. Another method of white balancing adjusts the image until the sum of all the areas in the image adds up to a neutral gray. Both of these methods operate on assumptions about the content of the scene.
Another method uses a correlation matrix memory to map the image data onto color image data under a number of different illuminants. This method is described in U.S. Pat. No. 6,038,339 “White point determination using correlation matrix memory” inventers Paul M. Hubel et al. that is hereby incorporated by reference. When using this method the image data needs to be mapped onto the color data for all potential illuminants. Mapping the image data onto each of the potential illuminants is a computational process. If the set of potential illuminants could be narrowed to the type of illuminant (for example daylight) the amount of computation, and therefore the time could be reduced. One way to narrow the set of potential illuminants is to determine if the scene contains artificial illumination. Therefore the ability to detect the presence of artificial illumination can increase the speed and accuracy of the color correction algorithms inside digital cameras.
Typically most artificial illumination sources are powered by alternating current. There are two main frequencies for alternating current. The United States uses 60 Hz and Europe uses 50 Hz. At these speeds, the human eye typically does not detect variations in the brightness of the artificial illuminant. However, digital cameras and other devices that detect light using today's photo sensors can and do detect the variation in brightness due to the alternating current (AC) driving most artificial illumination sources. The brightness variation typically is larger under fluorescent illumination sources and smaller under incandescent illumination sources. These variations in intensity can cause problems for some of the automatic functions in digital cameras like auto-focus and auto-exposure.
When using the auto-exposure function, the camera adjusts the lens aperture, the exposure length and gain of the photo sensor to gather the correct amount of light for a proper exposure. The auto-exposure function relies on accurate measurements of the amount of light within the scene to set the exposure parameters. The exposure lengths for photo sensors, typically a CCD, when measuring light for the automatic-exposure function has a typical range from {fraction (1/60)} to {fraction (1/1000)} of a second. Exposure measurement errors can be large if the exposure lengths are smaller than the period of the driving frequency of the AC power source. When scene illumination varies because of artificial illumination, incorrect final image exposure may result if the variation in intensity is not taken into account.
When using the auto-focus function, the camera adjusts the position of the lens to focus the scene on the photo sensor. Typically cameras use a measure of contrast between areas in the scene to determine proper focus. The auto focus algorithm typically takes multiple exposures of a scene with the lens in different positions, and then selects the lens position corresponding to the exposure with the highest contrast. Unfortunately the level of illumination in the scene affects the contrast in a scene. This can result in a high focus-contrast measurement during a bright part of the artificial light source and a low focus-contrast measurement during a dimmer part of the light source cycle. If the light is brighter during an out-of-focus focus-contrast measurement, the out-of-focus position may be chosen as best unless this variation in intensity is taken into account. Therefore there is a need for a system that can determine and correct for the presence of artificial illumination in a scene.